File Organizations and Indexing - PowerPoint PPT Presentation

Loading...

PPT – File Organizations and Indexing PowerPoint presentation | free to download - id: 786f94-ZDIwY



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

File Organizations and Indexing

Description:

Lecture 4 R&G Chapter 8 – PowerPoint PPT presentation

Number of Views:3
Avg rating:3.0/5.0
Slides: 26
Provided by: berk134
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: File Organizations and Indexing


1
File Organizations and Indexing
  • Lecture 4
  • RG Chapter 8

"If you don't find it in the index, look very
carefully through the entire catalogue." --
Sears, Roebuck, and Co., Consumer's Guide, 1897
2
Review Memory, Disks, Files
  • Everything wont fit in RAM (usually)
  • Hierarchy of storage, RAM, disk, tape
  • Block - unit of storage in RAM, on disk
  • Allocate space on disk for fast access
  • Buffer pool management
  • Frames in RAM to hold blocks
  • Policy to move blocks between RAM disk
  • Storing records within blocks

3
Today File Storage
  • How to keep blocks of records on disk
  • but must support operations
  • scan all records
  • search for a record id RID
  • insert new records
  • delete old records

4
Alternative File Organizations
  • Many alternatives exist, each good for some
    situations, and not so good in others
  • Heap files Suitable when typical access is a
    file scan retrieving all records.
  • Sorted Files Best for retrieval in search key
    order, or only a range of records is needed.
  • Clustered Files (with Indexes) Coming soon

5
Cost Model for Analysis
  • We ignore CPU costs, for simplicity
  • B The number of data blocks
  • R Number of records per block
  • D (Average) time to read or write disk block
  • Measuring number of block I/Os ignores gains of
    pre-fetching and sequential access thus, even
    I/O cost is only loosely approximated.
  • Average-case analysis based on several
    simplistic assumptions.
  • Good enough to show the overall trends!

6
Some Assumptions in the Analysis
  • Single record insert and delete.
  • Equality selection - exactly one match (what if
    more or less???).
  • Heap Files
  • Insert always appends to end of file.
  • Sorted Files
  • Files compacted after deletions.
  • Selections on search key.

7
Cost of Operations
B The number of data pages R Number of
records per page D (Average) time to read or
write disk page
Heap File Sorted File Clustered File
Scan all records
Equality Search
Range Search
Insert
Delete
8
Cost of Operations
B The number of data pages R Number of
records per page D (Average) time to read or
write disk page
Heap File Sorted File Clustered File
Scan all records BD BD
Equality Search
Range Search
Insert
Delete
9
Cost of Operations
B The number of data pages R Number of
records per page D (Average) time to read or
write disk page
Heap File Sorted File Clustered File
Scan all records BD BD
Equality Search 0.5 BD (log2 B) D
Range Search
Insert
Delete
10
Cost of Operations
B The number of data pages R Number of
records per page D (Average) time to read or
write disk page
Heap File Sorted File Clustered File
Scan all records BD BD
Equality Search 0.5 BD (log2 B) D
Range Search BD (log2 B) match pgD
Insert
Delete
11
Cost of Operations
B The number of data pages R Number of
records per page D (Average) time to read or
write disk page
Heap File Sorted File Clustered File
Scan all records BD BD
Equality Search 0.5 BD (log2 B) D
Range Search BD (log2 B) match pgD
Insert 2D ((log2B)B)D (because R,W 0.5)
Delete
12
Cost of Operations
B The number of data pages R Number of
records per page D (Average) time to read or
write disk page
Heap File Sorted File Clustered File
Scan all records BD BD
Equality Search 0.5 BD (log2 B) D
Range Search BD (log2 B) match pgD
Insert 2D ((log2B)B)D
Delete 0.5BD D ((log2B)B)D (because R,W 0.5)
13
Indexes
  • Sometimes, we want to retrieve records by
    specifying the values in one or more fields,
    e.g.,
  • Find all students in the CS department
  • Find all students with a gpa gt 3
  • An index on a file is a disk-based data structure
    that speeds up selections on the search key
    fields for the index.
  • Any subset of the fields of a relation can be the
    search key for an index on the relation.
  • Search key is not the same as key (e.g. doesnt
    have to be unique ID).
  • An index contains a collection of data entries,
    and supports efficient retrieval of all records
    with a given search key value k.

14
First Question to Ask About Indexes
  • What kinds of selections do they support?
  • Selections of form field ltopgt constant
  • Equality selections (op is )
  • Range selections (op is one of lt, gt, lt, gt,
    BETWEEN)
  • More exotic selections
  • 2-dimensional ranges (east of Berkeley and west
    of Truckee and North of Fresno and South of
    Eureka)
  • Or n-dimensional
  • 2-dimensional distances (within 2 miles of Soda
    Hall)
  • Or n-dimensional
  • Ranking queries (10 restaurants closest to
    Berkeley)
  • Regular expression matches, genome string
    matches, etc.
  • One common n-dimensional index R-tree
  • Supported in Oracle and Informix
  • See http//gist.cs.berkeley.edu for research on
    this topic

15
Index Classification
  • What selections does it support
  • Representation of data entries in index
  • i.e., what kind of info is the index actually
    storing?
  • 3 alternatives here
  • Clustered vs. Unclustered Indexes
  • Single Key vs. Composite Indexes
  • Tree-based, hash-based, other

16
Alternatives for Data Entry k in Index
  • Three alternatives
  • Actual data record (with key value k)
  • ltk, rid of matching data recordgt
  • ltk, list of rids of matching data recordsgt
  • Choice is orthogonal to the indexing technique.
  • Examples of indexing techniques B trees,
    hash-based structures, R trees,
  • Typically, index contains auxiliary information
    that directs searches to the desired data entries
  • Can have multiple (different) indexes per file.
  • E.g. file sorted by age, with a hash index on
    salary and a Btree index on name.

17
Alternatives for Data Entries (Contd.)
  • Alternative 1 Actual data record (with key
    value k)
  • If this is used, index structure is a file
    organization for data records (like Heap files or
    sorted files).
  • At most one index on a given collection of data
    records can use Alternative 1.
  • This alternative saves pointer lookups but can be
    expensive to maintain with insertions and
    deletions.

18
Alternatives for Data Entries (Contd.)
  • Alternative 2
  • ltk, rid of matching data recordgt
  • and Alternative 3
  • ltk, list of rids of matching data recordsgt
  • Easier to maintain than Alt 1.
  • If more than one index is required on a given
    file, at most one index can use Alternative 1
    rest must use Alternatives 2 or 3.
  • Alternative 3 more compact than Alternative 2,
    but leads to variable sized data entries even if
    search keys are of fixed length.
  • Even worse, for large rid lists the data entry
    would have to span multiple blocks!

19
Index Classification
  • Clustered vs. unclustered If order of data
    records is the same as, or close to, order of
    index data entries, then called clustered index.
  • A file can be clustered on at most one search
    key.
  • Cost of retrieving data records through index
    varies greatly based on whether index is
    clustered or not!
  • Alternative 1 implies clustered, but not
    vice-versa.

20
Clustered vs. Unclustered Index
  • Suppose that Alternative (2) is used for data
    entries, and that the data records are stored in
    a Heap file.
  • To build clustered index, first sort the Heap
    file (with some free space on each block for
    future inserts).
  • Overflow blocks may be needed for inserts.
    (Thus, order of data recs is close to, but not
    identical to, the sort order.)

Index entries
UNCLUSTERED
CLUSTERED
direct search for
data entries
Data entries
Data entries
(Index File)
(Data file)
Data Records
Data Records
21
Unclustered vs. Clustered Indexes
  • What are the tradeoffs????
  • Clustered Pros
  • Efficient for range searches
  • May be able to do some types of compression
  • Possible locality benefits (related data?)
  • ???
  • Clustered Cons
  • Expensive to maintain (on the fly or sloppy with
    reorganization)

22
Cost of Operations
B The number of data pages R Number of
records per page D (Average) time to read or
write disk page
Heap File Sorted File Clustered File
Scan all records BD BD 1.5 BD
Equality Search 0.5 BD (log2 B) D (logF 1.5B) D
Range Search BD (log2 B) match pgD (logF 1.5B) match pgD
Insert 2D ((log2B)B)D ((logF 1.5B)1) D
Delete 0.5BD D ((log2B)B)D (because R,W 0.5) ((logF 1.5B)1) D
23
Composite Search Keys
  • Search on a combination of fields.
  • Equality query Every field value is equal to a
    constant value. E.g. wrt ltage,salgt index
  • age20 and sal 75
  • Range query Some field value is not a constant.
    E.g.
  • age gt 20 or age20 and sal gt 10
  • Data entries in index sorted by search key to
    support range queries.
  • Lexicographic order
  • Like the dictionary, but on fields, not letters!

Examples of composite key indexes using
lexicographic order.
11,80
11
12
12,10
name
age
sal
12,20
12
bob
10
12
13,75
13
cal
80
11
ltage, salgt
ltagegt
joe
12
20
sue
13
75
10,12
10
20
20,12
Data records sorted by name
75,13
75
80,11
80
ltsal, agegt
ltsalgt
Data entries in index sorted by ltsal,agegt
Data entries sorted by ltsalgt
24
Summary
  • Many alternative file organizations exist, each
    appropriate in some situation.
  • If selection queries are frequent, sorting the
    file or building an index is important.
  • Hash-based indexes only good for equality search.
  • Sorted files and tree-based indexes best for
    range search also good for equality search.
    (Files rarely kept sorted in practice B tree
    index is better.)
  • Index is a collection of data entries plus a way
    to quickly find entries with given key values.

25
Summary (Contd.)
  • Data entries in index can be actual data records,
    ltkey, ridgt pairs, or ltkey, rid-listgt pairs.
  • Choice orthogonal to indexing structure (i.e.
    tree, hash, etc.).
  • Usually have several indexes on a given file of
    data records, each with a different search key.
  • Indexes can be classified as
  • clustered vs. unclustered
  • dense vs. sparse
  • Differences have important consequences for
    utility/performance.
About PowerShow.com